Power system state estimation is a critical function in energy control centers. It is necessary to know the state of a given network, grid or the like in order to calculate or project a number of factors, including but not limited to load, load shedding, efficiency, power distribution, and fault prediction. State estimators require two kinds of information, measurement data which is accumulated (telemetered) and the electrical network model. Both measurement and model data are required to construct an accurate picture of the state of the system.
Accordingly, errors in either the telemetered data or the network model can seriously degrade the performance of a state estimator. Such errors have a non-linear effect on the correctness of state estimation values., such errors in even a modest 100 bus network, for example, can produce errors ranging from a nominal amount to forty percent. Therefore, depending on the size or number of busses, parameter errors can have similar or more adverse impact on state estimation than actual measurement errors.
Moreover, it has been found that undetected errors in branch parameters may severely affect the accuracy of network optimization as well as security applications, thereby reducing their effectiveness and, in some circumstances, leading to harmful control actions. Such actions may include, for example, altering the settings on tap changers, power factor correction, load shedding and the like.
Further, due to the recent efforts to deregulate the utility industry, estimation of branch impedance parameters is gaining more attention in power system analysis. Therefore, a key component of comprehensive estimation in energy management systems along with state and topology estimation is accurate impedance estimation.
Over time, there have been a number of investigators which have studied the problem of parameter estimation. These investigations have resulted in three basically different approaches being used. One such approach is that of the estimation of network parameters using a single data scan (one time point) of measurement data. However, this is impractical and prone to errors since there are rarely enough measurements in the vicinity of uncertain parameters to enable their estimation with just a single scan of measurements.
A second approach has been tried in which a set of measurements at multiple time points is used. A batch processing system algorithm is then used to solve for parameter estimates. However, this second approach is problematic since batch processing algorithms are well suited for off-line studies but are not amenable to on-line applications.
A third approach is to use a recursive algorithm based on the use of a Kalman filter. This approach models the bus voltage and angle variables as Markov processes and the network parameters as constants. This third approach is better for on-line estimations since the use of dynamic models allows one to use a recursive estimation in which apriori information about the state and parameter estimates is combined with current measurement data in order to update the parameter estimates.
Unfortunately, computational experience has indicated that the problem, as formulated by this third approach has the potential for convergence problems when it is applied to problems with large networks and/or several uncertain parameters. Moreover, the third approach treats network parameters as constants. This is simply not an acceptable tradeoff in an on-line system. More particularly, system parameters change for a number of reasons such as component aging over time, weather, environment and the like. Therefore, this limits the flexibility of the approach since some network parameters, such as corona losses, are time varying.
More recently, it has been proposed to use parameter estimation based on the analysis of state estimator measurement residuals. In this approach the state estimation and parameter estimation problems are solved separately. However, this approach is based on a single scan of measurements and therefore suffers from observability problems.
Accordingly, it is an object of the present invention to produce an energy management system which allows for estimation of all branch impedance parameters, i.e. transfer susceptance, transfer conductance, charging and corona losses.
It is another object of the present invention to produce an energy management system which it supports all main branch models; balanced Pi without corona losses, unbalanced Pi without corona losses, balanced Pi with corona losses, LTC and phase shifting transformer models.
Still a further object of the present invention is to produce an energy management system which allows for accurate tracking of branch parameters that continuously change due to changes in loading and ambient conditions, such as resistance, corona losses, charging and tap position parameters.
Yet another object of the present invention is to produce an energy management system which solution is not affected by changes in network and measurement topology, while unrestricted topology changes in measurement samples are permitted.
It is another object of the present invention to produce an energy management system which can be used in both an off-line and an on-line environment.
It is still another object of the present invention to produce an energy management system which has no dependencies on results of state estimation or other network applications and can be implemented in a Supervisory Control and Data Acquisition (SCADA) environment.
A further object of the present invention is to produce an energy management system which supports tap estimation and facilitates estimation of impedance curves for LTC and phase shifting transformers.
It is another object of the present invention to produce an energy management system which in an on-line environment, estimated branch parameters can be automatically fed back into the data base for use in other optimization and security network applications.
A still further object of the present invention to produce an energy management system which provides fast execution by solving several small problems rather than one large problem with much lower overall matrix fill-in.
Yet another object of the present invention is to produce an energy management system which does not presume that some branches have accurate parameters that can be used as known quantities for estimation of other branches.
Still a further object of the present invention is to produce an energy management system which supports adaptive expansion of coverage areas where branches with high measurement redundancy are estimated first until their parameters are established, followed by branches with low redundancy.
Finally, it is another object of the present invention to produce an energy management system and method for recursive parameter energy management control, comprising measurement means for measuring the status and settings of a power grid or subsystem (grid) and producing representative grid data thereof, a computer having memory means for storing said grid data and program means for analyzing said grid data and producing values representative of the impedance parameters of said grid, said program means including means for separating said grid data into a plurality of sub-grid data sets, each of said sub-grid data sets containing a predetermined number of unknown grid impedance parameters and means for recursively modeling each of said sub-grid data sets into Markov processes. Such a system and method is taught by the present invention.